CU Medicine reaches 92% radiology coding automation with CodaMetrix, cutting coding lag by 3.6 days
CU Medicine was constrained by a 46% automation ceiling in radiology coding and suffered from coding lag that slowed operations, while needing to handle high visit volumes without adding headcount.
Prior automation hit a ceiling at 46%, unable to push radiology coding automation higher.
CU Medicine reached 92% radiology coding automation, cut coding lag by 3.6 days, and handled 6,000 visits a day without adding headcount, while also decreasing costs.
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Frequently asked questions
What did this team achieve with this AI workflow?
CU Medicine reached 92% radiology coding automation, cut coding lag by 3.6 days, and handled 6,000 visits a day without adding headcount, while also decreasing costs.
What tools did this team use?
CodaMetrix.
What results were reported?
Previous automation ceiling: 46%; Radiology coding automation rate: 92%; Coding lag reduction: 3.6 days; Daily visit volume handled: 6,000 visits a day (source-reported, not independently verified).
What failed first in this deployment?
Prior automation hit a ceiling at 46%, unable to push radiology coding automation higher.
How is this medical records processing AI workflow structured?
High-volume visit intake → Contextual coding automation → Coded record output.